Accelerated Graph Learning from Smooth Signals

Seyed Saman Saboksayr; Gonzalo Mateos

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arXiv · 2110.09677

Accelerated Graph Learning from Smooth Signals

Seyed Saman Saboksayr·Gonzalo Mateos

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Abstract

We consider network topology identification subject to a signal smoothness prior on the nodal observations. A fast dual-based proximal gradient algorithm is developed to efficiently tackle a strongly convex, smoothness-regularized network inverse problem known to yield high-quality graph solutions. Unlike existing solvers, the novel iterations come with global convergence rate guarantees and do not require additional step-size tuning. Reproducible simulated tests demonstrate the effectiveness of the proposed method in accurately recovering random and real-world graphs, markedly faster than state-of-the-art alternatives and without incurring an extra computational burden.

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Seyed Saman Saboksayr, Gonzalo Mateos. 2021-10-19. Accelerated Graph Learning from Smooth Signals. https://arxiv.org/abs/2110.09677

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